1. Field of the Invention
This invention relates to a recognition system and method for category classification, which constitutes a problem when a recognition dictionary or a decision tree structure should be automatically prepared. Specifically, the invention relates to a recognition system and method for category classification permitting classification of a plurality of categories or classes for a certain feature (into small groups consisting of the smallest unit, while maintaining a satisfactory security) in a period of time as short as possible (whatever is the distribution situation of the plurality of categories which are objects of each of the nodes of the tree).
2. Description of the Prior Art
The classification of the tree structure is described in "SRI VISION RESEARCH FOR ADVANCED INDUSTRIAL AUTOMATION" by Gerald J. AGIN and Richard O. DUDA, Second USA-JAPAN Computer Conference, 1975.
In general, identification of a pattern or object which is to be recognized is effected on the basis of a recognition dictionary previously prepared. The recognition dictionary is prepared b analyzing distribution data of every category or class after having several times extracted various features for each of the objects to be recognized. In this case it is desirable to prepare automatically the recognition dictionary mentioned above by a certain standardized method in order to alleviate analysis work and to exclude the subjectivity of the analyzer. As a structure of the recognition dictionary, the structure realizing identification by means of a decision tree (decision tree structure dictionary), which attaches importance to treatment speed, is the most prominent. However a problem which is always confronted always at the time of the preparation of the decision tree structure dictionary is how the ranking of features should be decided in order to evaluate the features which are to be given to each of the nodes of the tree and how a plurality of categories, which are objects to be recognized, can be classified into a plurality of small groups while maintaining their reliability, i.e. security, on the axis of the features which are to be given to each of the nodes of the tree. Hereinbelow the division of the plurality of categories, which are objects to be recognized, at the gaps in their distribution into small groups on the feature axis mentioned above is called category classification. To classify the objects into a plurality of small groups while maintaining security is used here in the meaning that where the frequency is small in the frequency distribution, although there exists mutual interference or distribution superposition between different categories, it is neglected and they are decided or judged to be independent.
Known methods for feature ranking are known the separability method (e.g. "Oyo gazo kaiseki (Applied image analysis) by Jumpei Tsujiuchi, Kyoritsu Shuppan Co., Ltd.), the stability coefficient method (e.g. Provisional publication of patent application No. 25078/1982), and the variance ratio method (e.g. "Mathematical study on feature extraction in the pattern recognition (in Japanese) " by Otsu, Electrotechnical Laboratory Headquarters Research Report No. 818.) Hereinbelow each of them will be briefly explained.
Suppose now that category groups, which are to be classified (identified) are C.sub.a -C.sub.d as indicated in FIGS. 1a, 1b and 1c and that features prepared for them are F.sub.1 -F.sub.3. The frequency distribution concerning a feature F.sub.1 of a plurality of samples of objects contained in a category C.sub.a, i.e. the mean value .mu..sub.a and the standard deviation .sigma..sub.a, can be obtained. The mean value and the standard deviation of the other categories C.sub.b -C.sub.d are also obtained, as indicated in FIG. 1a. Also for the features F.sub.2 and F.sub.3 their frequency distribution is obtained, as indicated in FIGS. 1b and 1c, respectively. (The value D.sub.c obtained according to this invention and indicated in the figures will be described later.) As a feature, e.g. for a circular part, the length of its periphery can be used. Different distribution curves as indicated in FIGS. 1a-1c are obtained due to fluctuations in light intensity, i.e. brightness, on the circular part.